Task 1 involves analyzing eight different datasets named “Data1.csv” through “Data8.csv”. For each dataset, the task requires applying K-means and hierarchical clustering algorithms to generate clusters and evaluating their performance using external validation metrics. The next step involves plotting the data points in 2D or 3D and color-coding them based on their original class and the class allocated by the clustering algorithm.
Task 2 involves working with a world indicators dataset that compares various countries based on selected attributes. The task involves using K-means and hierarchical clustering methods to group similar countries, assessing the quality of the clusters using internal validation metrics, reporting the best clustering solution, and providing a detailed list of all the groups and the countries included within them. Additionally, the task requires generating three different scatter plots of choice and color-coding the data points based on their group membership, such as "Life expectancy vs GDP" or "Infant Mortality vs GDP."